docarray
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‎docarray/index/backends/qdrant.py‎
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‎docarray/index/backends/qdrant.py‎
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‎tests/index/qdrant/fixtures.py‎
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‎tests/index/qdrant/fixtures.py‎
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‎tests/index/qdrant/test_external_collection.py‎
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‎tests/index/qdrant/test_external_collection.py‎
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+64Lines changed: 64 additions & 0 deletions
@@ -612,7 +612,11 @@ def _convert_to_doc(
         self, point: Union[rest.ScoredPoint, rest.Record]
     ) -> Dict[str, Any]:
         document = cast(Dict[str, Any], point.payload)
-        generated_vectors = document.pop('__generated_vectors')
+        generated_vectors = (
+            document.pop('__generated_vectors')
+            if '__generated_vectors' in document
+            else []
+        )
         vectors = point.vector if point.vector else dict()
         if not isinstance(vectors, dict):
             vectors = {'__default__': vectors}
 
@@ -29,7 +29,8 @@ def tmp_collection_name():
 def qdrant() -> qdrant_client.QdrantClient:
     """This fixture takes care of removing the collection before each test case"""
     client = qdrant_client.QdrantClient(path='/tmp/qdrant-local')
-    client.delete_collection(collection_name='documents')
+    for collection in client.get_collections().collections:
+        client.delete_collection(collection.name)
     return client
 
 
 
@@ -0,0 +1,64 @@
+from docarray import BaseDoc
+from docarray.index import QdrantDocumentIndex
+from docarray.typing import NdArray
+from tests.index.qdrant.fixtures import qdrant, qdrant_config  # noqa: F401
+
+from qdrant_client.http import models
+
+
+def test_external_collection_without_generated_vectors(qdrant_config):
+    class Restaurant(BaseDoc):
+        city: str
+        price: float
+        cuisine_vector: NdArray[4]
+
+    qdrant_config.collection_name = 'test'
+    doc_index = QdrantDocumentIndex[Restaurant](qdrant_config)
+    qdrant_client = doc_index._client
+
+    qdrant_client.recreate_collection(
+        collection_name='test',
+        vectors_config={
+            'cuisine_vector': models.VectorParams(
+                size=4, distance=models.Distance.COSINE
+            )
+        },
+    )
+
+    qdrant_client.upsert(
+        collection_name='test',
+        points=[
+            models.PointStruct(
+                id=1,
+                vector={'cuisine_vector': [0.05, 0.61, 0.76, 0.74]},
+                payload={
+                    'city': 'Berlin',
+                    'price': 1.99,
+                },
+            ),
+            models.PointStruct(
+                id=2,
+                vector={'cuisine_vector': [0.19, 0.81, 0.75, 0.11]},
+                payload={
+                    'city': 'Berlin',
+                    'price': 1.99,
+                },
+            ),
+            models.PointStruct(
+                id=3,
+                vector={'cuisine_vector': [0.36, 0.55, 0.47, 0.94]},
+                payload={
+                    'city': 'Moscow',
+                    'price': 1.99,
+                },
+            ),
+        ],
+    )
+
+    results = doc_index.find(
+        query=[0.36, 0.55, 0.47, 0.94],
+        search_field='cuisine_vector',
+        limit=3,
+    )
+
+    assert results is not None